Improving Effectiveness of a Coaching System through Preference Learning

Abstract

The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities of positive feedback as assigned by a predictive machine learned model of user feedback. The motivation and aim of such an indirect approach is to allow for preference estimation without burdening the users with interactive preference elicitation processes. A brief description of the coaching setting is provided in the paper, before the approach for preference assessment is described and illustrated on a real-world example obtained during the testing of the coaching system with elderly users.

Keywords

• machine learning
• preference learning
• user centered design